Efficient algorithms and protocols for energy conservation of wireless sensor networks: A survey and future directions

 Corresponding author: Mohanad Ali Meteab Al-Obaidi. Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Efficient algorithms and protocols for energy conservation of wireless sensor networks: A survey and future directions Shajan M. Mahdi, Basim Jamil Ali and Mohanad Ali Meteab Al-Obaidi * Department of Computer Science, College of Education, Mustansiriyah University, Baghdad Iraq. Global Journal of Engineering and Technology Advances, 2025, 22(02), 027–033 Publication history: Received on 23 December 2024; revised on 02 February 2025; accepted on 05 February 2025 Article DOI: https://doi.org/10.30574/gjeta.2025.22.2.0022 Abstract Wireless sensor networks (WSNs) play an increasing role in very large-scale Health applications, environmental monitoring, industrial automation, science and military and agricultural industries, as well as smart and large cities. and large-scale applications. These nodes have limited resources such as limited memory and power. These nodes are responsible for transmitting data in real-time and must be secure and reliable. An important consideration is how to keep the energy of these nodes running efficiently for as long as possible. These requirements pose a major challenge in selecting appropriate protocols for routing and data transmission in a secure and continuous manner without interruption. Previous studies have focused on innovating and developing energy-saving algorithms and ensuring network scalability using machine learning methods to predict energy consumption behavior. This paper focuses on reviewing energy-optimizing algorithms and their types, energy-minimizing protocols and their working mechanisms, strengths, and weaknesses, as well as the relationship between energy and energy-optimizing encryption algorithms. Keywords: WSN; PEGASIS; TEEN; EADAT; LEACH; Gathering; Grain; Trivium 1. Introduction Wireless sensor networks have become the cornerstone of modern technological systems [1, 2]. WANs are used in healthcare applications, smart cities, military science, industrial automation, and environmental monitoring such as ecological pollution, different climatic conditions, desertification, and weather conditions such as temperature, humidity, earthquakes, and volcanoes [3,4]. Sensor networks consist of wireless sensors interconnected together that operate in different environmental conditions (hot and cold), remote areas, and for long periods. Therefore, the main task that must be studied is how to conserve battery power so that the network operates efficiently and extends its life [5,6]. Naturally, the wireless sensor unit consists of four components, which are the sensor unit, the processor unit, and the communication unit, in addition to the battery. The most energy-consuming unit is the communication unit that sends and receives data [7]. The battery power of sensor nodes has limited energy and it is essential how to maintain this energy for the longest period to increase the life of the network. This energy depends on communication and transport protocols, routing protocols, data collection methods, and network distribution and management methods [8,9]. The need for energy-efficient algorithms for wireless sensor networks has become a major concern for developers because it is difficult to replace the batteries of thousands of nodes spread across multiple locations. Another important reason is that the failure of one battery to work leads to the neutralization of the rest of the nodes and their failure to send and receive data and perhaps the failure of the system to continue. [10]. In recent years, researchers have been directed to develop advanced protocols and algorithms in the field of network energy by balancing energy consumption and reducing duplicate data operations and unjustified energy consumption to some extent so that these algorithms do not affect the efficiency of the network and the energy of idle nodes can be utilized by scheduling nodes according to certain criteria without compromising the optimal performance of the network [11,12]. The interplay between Global Journal of Engineering and Technology Advances, 2025, 22(02), 027–033 28 hardware and software components in wireless sensor networks significantly determines energy efficiency. Devices with limited power and processing capabilities often face challenges when executing complex algorithms or processing extensive data streams. Consequently, this scarcity compels developers to innovate energy-efficient solutions in both software design and hardware architecture. For instance, software optimization strategies, such as compression algorithms and energy-aware scheduling, can dramatically reduce the processing load on constrained devices. In parallel, advancements in hardware, including low-power circuits and application-specific integrated circuits (ASICs), directly contribute to reduced energy consumption during data transmission and processing tasks. The adoption of a cloud/edge communication architecture has gained attention in this context, as it can offload computation-heavy tasks away from the device, which can extend battery life while maintaining operational efficiency [13]. Therefore, understanding and addressing the synergy between hardware and software is essential for advancing energy efficiency in these networks [14]. Moving forward, researcher must focus on developing hybrid methodologies that fuse existing protocols with innovative energy-efficient strategies, ensuring seamless scalability. Additionally, exploring the potential of machine learning algorithms to predict energy consumption patterns could provide valuable insights into optimizing network performance. Ultimately, addressing these future trends is vital to promoting the design and implementation of sustainable wireless sensor networks. 2. Related Works Rajiv Yadav et. al., in 2022 they presented research in which they used hybrid algorithms (DE-GA, GA-PSO, PSO-ACO, PSO-ABC, PSO-GWO, etc.) to produce advanced biologically inspired techniques to provide a solution to improve the efficiency of energy-efficient networks based on node location, sensor coverage area, and data collection. Variables such as power, energy loss, data transmission delay, and overhead were used. They were also studied to improve QoS, wireless network stability, and secure transmission lines [15]. Qianao Ding et al. in 2021 developed a virtual machine learning model to produce an energy-efficient green routing model in wireless sensor networks to improve the efficiency of wireless sensor networks and address the barriers to green routing strategies. Energy-efficient routing algorithms are classified into a set of categories such as data flow optimization according to singleor multi-path routing paths, hierarchical node selection, and energy-efficient node scheduling [16]. The researchers Lucia K. Ketshabetswe et. al. In 2024, he presented a study to improve the energy consumption of wireless sensor networks for lossless data compression using (ALDC) and (FELACS) to reduce the number of bits required for encoding. This new technique improved the energy from 73% to 77% and is effective in identifying and replacing outliers, which improved the performance of compression results [17]. Neda Nilsaz Dezfuli, et.al., in 2024 presented a method based on dividing the WSN network area into square areas then, searching for the nodes with the highest energy, identifying them and keeping them using the Firefly algorithm and using Omnet++ to study the simulation and compare the results with the TCO, ACO-Greedy models and deactivating other nodes, which resulted in a 30% reduction in energy demand [18].In 2021, researchers Zahid Yousuf et al. proposed an improved version of the LEACH protocol. They called it LEACH-PRO, with the aim of extending the network lifetime by calculating the distance between CH and BS, in addition to the remaining energy. This method has proven superior to previous methods in extending the network lifetime and is more flexible in smart city applications, as the network can be expanded [19]. Behzad Saemi, et.al., they proposed a hybrid metaheuristic algorithm called the global search algorithm (GSA) which combines the local search algorithm (LSA) and GSA. This hybrid algorithm aims to search for the best optimal path to the transmission area and reduce the time during the GSLS operation, which leads to reduced energy consumption and increased efficiency [20]. Adumbabu et.al. 2022 in their paper relied on a dynamic variable cluster head and relied on an algorithm consisting of three stages: preparation, transfer, and measurement, which they called the improved Coyote Optimization Algorithm (ICOA) to determine the best ideal path between the BS and the communication channel in light of the node rank and the remaining energy in addition to the distance between them. The case was studied on 100 and 200 and the results were very good in extending the network lifetime compared to previous methods based on TEEN and PEGASIS [21]. Global Journal of Engineering and Technology Advances, 2025, 22(02), 027–033 29 3. Motivation Energy-saving algorithms are essential in building WSNs to extend the network life for a longer period of time because it mainly depends on the power source, and replacing batteries in a wide area is difficult and expensive, so energysaving algorithms are most important in WSNs. Below is a set of algorithms as shown in Table 1 Table 1 Algorithm and key technique in WSN RE. No. Algorithm key technique [22] Duty Cycling Algorithms Sensor Medium Access Control (S-MAC). Timeout MAC (T-MAC) Berkeley MAC (B-MAC) [23] Energy-Efficient Routing Algorithms Low-Energy Adaptive Clustering Hierarchy (LEACH) Power-Efficient GAthering in Sensor Information Systems (PEGASIS) Threshold-sensitive Energy Efficient Network (TEEN) [24] Data Aggregation Algorithms Tiny Aggregation (TAG) Synopsis Diffusion. Energy-Aware Data Aggregation Tree (EADAT) [25] Clustering Algorithms LEACH. Hybrid Energy-Efficient Distributed Clustering (HEED) Distributed Energy-Efficient Clustering (DEEC) [26] Power Control and Topology Management Algorithms Geographical Adaptive Fidelity (GAF) SPAN Connected Coverage Set (CCS) [27] Machine Learning and AI-Based Algorithms Reinforcement Learning-Based Routing Deep Learning for Energy Prediction 4. Energy Efficiency in WSNs The energy efficiency in WSNs can be studied in terms of routing protocol algorithms and encryption algorithms : 4.1. Energy-Efficient Algorithms for WSNs Since energy is important to the lifetime of a wireless sensor network, we review some of the most energy-efficient algorithmic protocols that ultimately aim to improve energy consumption and latency speed for sending or receiving data. Table2 illustrate the types of protocols of energy efficiency in WSN. Table 2 Protocols of energy efficiency Protocol Description Mechanism Energy Efficiency Weaknesses Low-Energy Adaptive Clustering Hierarchy (LEACH) [28]. It is a hierarchical clustering protocol and each cluster of network has a random node CH that transmits data to the BS. 1. Cluster head randomly selected based on the power intensity 2. Data receiving and sending to BS Changing communication channels between nodes to reduce energy drain in longdistance transmissions. 1.Not suitable for large networks 2. A CH may be an inefficient node PowerEfficient Nodes form a chain, pass data to The nodes are organized in a chain, In this type, multihop communication Organizing nodes in a chain poses a Global Journal of Engineering and Technology Advances, 2025, 22(02), 027–033 30 GAthering in Sensor Information Systems (PEGASIS) [29]. their nearest neighbors, aggregate data at an intermediate node CH and send data to the BS and each node transmits data to its neighbor, each CH node is periodically assigned. is adopted to save energy. The intermediate nodes reduce the amount of data sent each time, and it is better than LEACH in saving energy. barrier to sending data to multiple nodes in addition to increasing the delay Geographical Adaptive Fidelity (GAF) [30] The WSNs are divided into several levels such that one level remains active and the rest of the nodes are in a sleep state A single node is active and changes periodically to ensure even power consumption This type is effective and contributes to reducing energy and is used in large networks and contributes to communication efficiency Inefficient for mobile networks Threshold Sensitive Energy Efficient Sensor Network Protocol (TEEN) [31] This hierarchical protocol is designed to handle timesensitive, eventdriven data transmission Follows a hierarchy where communication channels are two-level and transmit data when they reach a threshold to continuously reduce data transmission at the lowest event Suitable when data is frequent and constant. Energyefficient because data is transmitted in critical situations. Not suitable for variable data applications. Distributed EnergyEfficient Clustering (DEEC) [32] This type is energy efficient and suitable for disparate and heterogeneous WSNs. Based on the average available energy in the total network and the remaining energy of the nodes, the CH will be selected. The main function of the channel is to collect data from subnode and send data to BS, to reduce power consumption. Not suitable for wide area networks, ignores the distance between the communication channel and the base station, and high power consumption. Stable Election Protocol )SEP ( [33] It is suitable for heterogeneous networks and aims to reduce power consumption, extend the lifetime of the network, and address nodes with varying power levels Cluster heads are elected based on the remaining energy levels of other nodes. This technique is energy saving. It is energy efficient for heterogeneous networks and ensures that node do not die quickly because the network is trained to balance the nodes' energy and select the cluster head based on the available energy. Increased energy consumption for homogeneous networks with constantly changing data 4.2. Energy Efficiency in terms of Encryption Algorithm All encryption and decryption operations require computational and processing operations. Since wireless sensor networks are resource-constrained, they can consume more overhead, which means power consumption, thus reducing the network lifetime. Below are the most important and least energy consuming encryption algorithms. 4.3. Advanced Network Encryption Standard It is one of the symmetric encryption algorithms, considered one of the least energy-consuming algorithms, and includes a key for encryption and another for decryption. It is regarded as one of the most secure and fastest algorithms and Global Journal of Engineering and Technology Advances, 2025, 22(02), 027–033 31 deals with large data, so it is suitable for various types of applications, especially in wireless applications, as it can deal with 128, 192, and 256-bit keys at a time. This algorithm is considered one of the lightweight algorithms as it can reduce mathematical computational complexity because it deals with small keys, which require a small number of encryption rounds, reduces energy consumption in wireless sensor networks, speeds up network operation, and detects and removes fake nodes [34,35.36]. 4.4. Lightweight Encryption This algorithm is considered one of the symmetric encryption algorithms and a widely important solution in WSN networks to secure data reduce energy consumption to a minimum and ensure extending the network life because these networks often deal with batteries and have the advantage of limited computational power with fewer rounds than other encryption algorithms such as AES, RSA and SHA because they have a limited memory size to deal with large encryption keys and therefore they are lightweight algorithms because they do not deal with the encryption of relatively small block packets and therefore these algorithms suffer from security vulnerabilities and this algorithm includes a group of encryption methods such as Grain, Trivium, Present, Speck, Simon and Hight [37,38,39]. 5. Conclusion In conclusion, WSNs are considered an important means suitable for various vital applications in healthcare, industrial automation, military industries, environmental monitoring, and smart cities. These networks still face several challenges such as limited node memory and energy. This paper focused on reviewing hybrid algorithms for energy reduction, their working mechanisms, and the strengths and weaknesses of each, in addition to the best encryption algorithms with operations that do not increase energy consumption. We hope that in the future, research will expand to find the best hybrid algorithms that are suitable for energy reduction, which ensures optimal network performance. Compliance with ethical standards Acknowledgments The researchers would like to thank Al-Mustansiriya University (www.uomustansiriyah.edu.iq) Baghdad-Iraq for their support in this research paper and for providing advice. Disclosure of conflict of interest No conflict of interest to be disclosed. References [1] Majid M, et. al. “Applications of Wireless Sensor Networks and Internet of Things Frameworks in the Industry Revolution 4.0: A Systematic Literature Review”, Sensors Journal, 8, 22(6):2087,2022, doi: 10.3390/s22062087. [2] Kandris, D., et. al., “Applications of Wireless Sensor Networks: An Up-to-Date Survey” Applied System Innovation Journal, 3(1):14 2020. https://doi.org/10.3390/asi3010014. [3] Duobiene S, et. al., Development of Wireless Sensor Network for Environment Monitoring and Its Implementation Using SSAIL Technology”, Sensors Journal, 22(14),5343, 2022, https://doi.org/10.3390/s22145343. [4] Lanzolla A, Spadavecchia M.,” Wireless Sensor Networks for Environmental Monitoring”, Sensors Journal. 7;21(4):1172, 2021, doi: 10.3390/s21041172. [5] Xu Y, Jiao W, Tian M. “Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks”, Sensors Journal, 20(21):6127, 2020, https://doi.org/10.3390/s20216127. [6] Jasim AA, at. el.,” Energy-Efficient Wireless Sensor Network with an Unequal Clustering Protocol Based on a Balanced Energy Method (EEUCB), Sensors Journal, 21(3):784, 2021, https://doi.org/10.3390/s21030784. [7] Ehizuenlen, P.E, Apeh, S.T, Design of a Smart Custom Sensor Node Architecture for Wireless Sensor Network (WSN)”, Journal of Science and Technology Research 5(2) 2023 pp. 133-140 ISSN-2682-5821, https://doi.org/10.5281/zenodo.8014261. [8] Long Huy Tran, et. al.,” Classification of Secure Routing Protocols in Wireless Sensor Networks”, Smart Systems and Devices Journal, ISSN: 2734-9373, 2022, https://doi.org/10.51316/jst.163.ssad.2023.33.1.3. Global Journal of Engineering and Technology Advances, 2025, 22(02), 027–033 32 [9] U. Srilakshmi, "Swarm Technology based Self-Deployment with ACO for Wireless Sensor Network in the Presence of Obstacles," 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Bhopal, India, 2021, pp. 494-498, https://doi.org/10.1109/CSNT51715.2021.9509712. [10] K. R. Rao, M. Z. U. Rahman, K. P. Satamraju and B. N. K. Reddy, "Genetic Algorithm for Cross-Layer-Based Energy Hole Minimization in Wireless Sensor Networks," in IEEE Sensors Letters, vol. 6, no. 12, pp. 1-4, Dec. 2022, Art no. 7005104, https://doi.org/10.1109/LSENS.2022.3219857. [11] Poonam T. Agarkar, et. al.,” A Comprehensive Survey on Routing Schemes and Challenges in Wireless Sensor Networks (WSN)”, International Journal of Computer Networks and Applications (IJCNA) Volume 7, Issue 6,2020 https://doi.org/10.22247/ijcna/2020/205320. [12] Y. Gong, X. Guo and G. Lai, "A Centralized Energy-Efficient Clustering Protocol for Wireless Sensor Networks," in IEEE Sensors Journal, vol. 23, no. 2, pp. 1623-1634, 15, 2023, https://doi.org/10.1109/JSEN.2022.3224180. [13] Aisha Iskakova, et. al., “Wearable Communications in 5G: Challenges and Enabling Technologies". IEEE Vehicular Technology Magazine arXiv:1708.05410(99), 2018, https://doi.org/10.48550/arXiv.1708.05410. [14] Daniele D'Agostino,” Hardware and Software Solutions for Energy-Efficient Computing in Scientific Programming’, Scientific Programming Journal, Vol. 2021, Article ID 5514284, 9 pages https://doi.org/10.1155/2021/5514284. [15] Yadav, Rajiv, Indu Sreedevi, Daya Gupta, "Bio-Inspired Hybrid Optimization Algorithms for Energy Efficient Wireless Sensor Networks: A Comprehensive Review" Electronics Journal, Vol.11, No. 10: 1545, 2022, https://doi.org/10.3390/electronics11101545. [16] Ding, Q.; Zhu, R.; Liu, H.; Ma, M. An Overview of Machine Learning-Based Energy-Efficient Routing Algorithms in Wireless Sensor Networks. Electronics Journal, 2021, 10(13), 1539; https://doi.org/10.3390/electronics10131539. [17] Lucia K. Ketshabetsw, et. al., “Energy-efficient algorithms for lossless data compression schemes in wireless sensor networks”, Scientific African Journal, Vol.23, e02008, 2024, https://doi.org/10.1016/j.sciaf.2023.e02008. [18] Neda Nilsaz Dezfuli, Hamid Barati,” Distributed Energy Efficient Algorithm for Ensuring Coverage of Wireless Sensor Networks”, IET Commun Jornal, Vol. 13 Iss. 5, pp. 578-584,2019, https://doi.org/10.1049/ietcom.2018.5329. [19] Yousif, Zahid, et. al., "A Novel Energy-Efficient Clustering Algorithm for More Sustainable Wireless Sensor Networks Enabled Smart Cities Applications" Journal of Sensor and Actuator Networks 10, no. 3: 50., 2021, https://doi.org/10.3390/jsan10030050. [20] Behzad Saemi, Fariba Goodarzian, “Energy-Efficient Routing Protocol for Underwater Wireless Sensor Networks Using A Hybrid Metaheuristic Algorithm”, Engineering Applications of Artificial Intelligence Journal, Vol.133, Part C, 108132,2024, https://doi.org/10.1016/j.engappai.2024.108132. [21] Adumbabu, and K. Selvakumar. "Energy Efficient Routing and Dynamic Cluster Head Selection Using Enhanced Optimization Algorithms for Wireless Sensor Networks" Energies Journal, Vol. 15, No. 21: 8016, 2022, https://doi.org/10.3390/en15218016. [22] Liangshun Wu, et.al., “Duty Cycle Scheduling in Wireless Sensor Networks Using an Exploratory StrategyDirected MADDPG Algorithm”, International Journal of Sensors and Sensor Networks, Vol. 12, No. 1, 2024, ,http://dx.doi.org/10.11648/j.ijssn.20241201.11. [23] Solayan Kaviarasan,”Developing A novel Energy Efficient Routing Protocol in WSN Using Adaptive Remora Optimization Algorithm”, Expert Systems with Applications Journal,Vol. 244, 2024, 122873, https://doi.org/10.1016/j.eswa.2023.122873. [24] Zhenpeng Liu,” Data Aggregation Algorithm for Wireless Sensor Networks with Different Initial Energy of Nodes”, Peer Journal for Computer Science, 2024, http://dx.doi.org/10.7717/peerj-cs.1932. [25] Senkumar, M.R., Arafat, I.S., Nathiya, R. et al.,” Enhanced Energy Efficient Clustering and Routing Algorithm in Wireless Sensor Network”, Wireless Pers Commun 2024, https://doi.org/10.1007/s11277-024-11549-7. [26] Sohail, Muhammad, et. al.,"Game Theoretic Solution for Power Management in IoT-Based Wireless Sensor Networks", Sensors Journal, 2019, No. 18: 3835. https://doi.org/10.3390/s19183835. Global Journal of Engineering and Technology Advances, 2025, 22(02), 027–033 33 [27] Salim El khediri.” Integration of Artificial Intelligence (AI) With Sensor Networks: Trends, Challenges, and Future Directions”, Journal of King Saud University - Computer and Information Sciences Vol. 36, Iss. 1, 2024, 101892. https://doi.org/10.1016/j.jksuci.2023.101892. [28] Salim El Khediri, et.al.,” MW-LEACH: Low Energy Adaptive Clustering Hierarchy Approach for WSN”, IET Wirel. Sens. Syst., 2020, Vol. 10 Iss. 3, pp. 126-129, https://doi.org/10.1049/iet-wss.2019.0195. [29] Kandrakunta Chinnaiah, Kunjam Nageswara Rao, “Swarm energy efficient power efficient gathering in sensor”, information systems protocol in wireless sensor networks Indonesian Journal of Electrical Engineering and Computer Science, Vol. 31, No. 1, July 2023, pp. 459~469, DOI: 10.11591/ijeecs.v31.i1.pp459-469. [30] Oluwaseun Ibrahim Akinola,” Adaptive Location-based Routing Protocols for Dynamic Wireless Sensor Networks in Urban Cyber-physical Systems”, Journal of Engineering Research and Reports Vol.26, Issue 7, https://doi.org/10.9734/jerr/2024/v26i71220. [31] Jaffri Z ul A, Asif M, Khan WU, et al. TEZEM: A new Energy-Efficient Routing Protocol for Next-Generation Wireless Sensor Networks. International Journal of Distributed Sensor Networks. 2022;18(6). doi:10.1177/15501329221107246. [32] F. Jibreel, ET. AL., “Distance-Distributed Energy Efficient Clustering (D-DEEC) Routing Protocol for Wireless Sensor Network”, Asian Journal of Research in Computer Science, Vol. 17, Iss. 1, 2024, DOI: 10.9734/AJRCOS/2024/v17i1410. [33] A. Hossan and P. K. Choudhury, "DE-SEP: Distance and Energy Aware Stable Election Routing Protocol for Heterogeneous Wireless Sensor Network," in IEEE Access, vol. 10, pp. 55726-55738, 2022, doi: 10.1109/ACCESS.2022.3177190. [34] Jamal Khudair,” Comparative Study in Enhancing AES Algorithm: Data Encryption”, Wasit Journal for Pure Science Vol. (2) No. (2), 2023, DOI: https://doi.org/10.31185/wjps.100. [35] Maryam Shahmanesh, Razieh Malekhosseini, Taher Niknam, et al.,” Towards a Coefficient Secure IoT Energy Framework within the Smart City: Advanced Encryption Standard”, Authorea Journal, 2023. DOI: 10.22541/au.167821870.06789848. [36] Biaoqi Li, et. al., “Optimization of Security Identification in Power Grid Data through Advanced Encryption Standard Algorithm”, Journal of Cyber Security and Mobility, Vol. 13, No. 02, pp. 239–264,2024, DOI: https://doi.org/10.13052/jcsm2245-1439.1323. [37] Animesh Srivastava,” Secure Authentication Scheme for the Internet of Things”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol.11, Iss.4s, DOI: https://doi.org/10.17762/ijritcc.v11i4s.6368. [38] Ntebatseng Mahlake,”A Lightweight Encryption Algorithm to Enhance Wireless Sensor Network Security on the Internet of Things”, Journal of Communications Vol. 18, No. 1,2023, doi:10.12720/jcm.18.1.47-5. [39] Ruah Mouad Alyas AL_AZZAWI,” A lightweight Encryption Algorithm to Secure IoT Devices”, International Journal of Applied Sciences and Technology, 2023, http://dx.doi.org/10.47832/2717-8234.16.3.